Module 1- What is IA? AI Applications and Examples

In this Module 1, you will learn what AI is. You will understand its applications, use cases and how it is transforming our lives.

Learning objectives:

• Define AI
• Describe examples, applications and impact of AI

Course:

What you have learned?

• IBM Research defines Artificial Intelligence (AI) as Augmented Intelligence, helping experts expand their capabilities while machines do the laborious work.
• AI learns by creating machine learning models based on the input provided and the desired results.
• AI can be described in different ways based on strength, breadth and application – Weak or Narrow AI, Strong or Generalized AI, Super or Conscious AI.
• AI is the fusion of many fields of study, such as Computer Science, Electronic Engineering, Mathematics, Statistics, Psychology, Linguistics, and Philosophy.
• AI-powered apps are making an impact in such diverse areas as Healthcare, Education, Transcription, Law Enforcement, Customer Service, Mobile & Social Media Apps, Financial Fraud Prevention, Patient Diagnostics, Testing Clinicians, and more.

Some of these applications include:

• Robotics and Automation, where AI makes it possible for robots to perceive unpredictable environments around them to decide the next steps.
• Airport Security, where AI makes it possible for X-ray scanners to flag images that may appear suspicious.
• Oil and Gas, where AI is helping companies analyze and classify thousands of rock samples to help identify the best places to drill for oil.

Some famous IBM applications in AI:

• Watson plays Jeopardy to win against two of its greatest champions, Ken Jennings and Brad Rutter.
• Watson collaborating with the Academy to bring an amplified Grammy experience to millions of fans.
• Watson collaborating with ESPN to serve 10 million users of the ESPN Fantasy App, sharing insights that help them make better decisions to win their weekly games.

To learn more about AI, check out these articles:

• AI is not magic: it’s time to demystify and apply [English].

AI Structure: Componentry and Process.

The Componentry

AI is being driven by modern software componentry:

1. A unified, modern data fabric: AI feeds on data, and therefore data must be prepared for AI. A data fabric acts as a logical representation of all data assets, on any cloud. It pre-organizes and labels data across the enterprise. Seamless access to all data is available through virtualization from the firewall to the edge.
2. A development environment and engine: A place to build, train, and run AI models. This enables end-to-end deep learning, from input to output. Machine learning models, help find patterns and structures in data that are inferred, rather than explicit. This is when it starts to feel like magic.
3. Human features: A mechanism to bring models to life, by connecting models and applications to human features like voice, language, vision, and reasoning.
4. AI management and exploitation: This enables you to insert AI into any application or business process, while understanding versions, how to improve impact, what has changed, bias, and variance. This is where your models live for exploitation and enables lifecycle management of all AI. Lastly, it offers proof and explain-ability for decisions made by AI.
   

The Process

With these components in hand, more organizations are unlocking the value of data. But to fully leverage AI, we must also understand how to adopt and implement the technology. For those planning the move, consider these fundamental steps first:

1. Identify the Right Business Opportunities for AI: The potential areas for adoption are vast:  customer service, employee/company productivity, manufacturing defects, supply chain spending, and many more. Anything that can be easily described, can be programmed. Once it’s programmed, AI will make it better. The opportunities are endless.
2. Prepare the Organization for AI: Organizations will require greater capacity and expertise in data science. Many of today’s repetitive and manual tasks will be automated, which will evolve the role of many employees. It’s rare that an entire role can be done by AI. But it’s also rare that none of the role could be enhanced by AI. All technology is useless without the talent to put it to use, so build a team of experts that will inspire and train others.
3. Select Technology & Partners: While it’s unlikely that the CEO will personally select the technology, the implication here is more of a cultural one. An organization should adopt many technologies, comparing, contrasting, and learning through that process. An organization should also choose a handful of partners that have both the skills and technology to deliver AI.
4. Accept Failures: If you try 100 AI projects, 50 will probably fail. But, the 50 that work will be more than compensate for the failures. The culture you create must be ready and willing accept failures, learn from them, and move onto the next. Fail-fast, as they say.

By Rob Thomas

Senior Vice President, IBM Cloud and Data Platform

Questions:

Which of the following is NOT a good way to define AI?

Which of the following are attributes of Strong or Generalized AI?

AI is the fusion of many fields of study. Which of these fields, along with Computer Engineering, plays a role in the application of AI?

Which of these are the current applications of AI?

Natural Language AI algorithms that learn by example are the reason we can talk to machines and they can answer us:

Advances in the field of Artificial Vision:

Which of these is NOT currently a Collaborative Robots or Cobots application?

Which of the following aspects involved in converting the stethoscope to a digital device to aid in patient diagnosis involves the use of AI?

Which of the following are the applications of Artificial Intelligence in action?